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Record W2064272750 · doi:10.1115/1.4027021

Multijoint Rigidity-Testing Device for Titrating Medication and Deep Brain Stimulation Therapies1

2014· article· en· W2064272750 on OpenAlexaboutno aff
Kevin Mohsenian, Allison T. Connolly, Matthew D. Johnson

Bibliographic record

VenueJournal of Medical Devices · 2014
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical medicine and rehabilitationDeep brain stimulationRigidity (electromagnetism)ElbowSubthalamic nucleusMuscle RigidityBrain stimulationNeuroscienceMotor symptomsMovement disordersRating scaleParkinson's diseaseMedicinePsychologyDiseaseStimulationSurgeryPhysicsPathology

Abstract

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Parkinson's disease (PD) is a neurodegenerative brain disorder that results in a broad range of disabling motor signs, including akinesia, bradykinesia, tremor, and muscle rigidity. Typically, a clinician will quantify the severity of these motor signs from 0 (normal) to 4 (severe) with a unified Parkinson's disease rating scale (UPDRS, Sec. 3). These subjective assessments, while useful, often vary among clinicians [1], making it challenging to evaluate medication and deep brain stimulation (DBS) therapies in multicenter trials.Several previous studies have developed biomechanical devices to measure muscle rigidity in human [1–3] and nonhuman primates [4] using motorized actuators that can be selectively programmed to articulate the elbow joint across a range of angles and frequencies. However, correlating the results from these studies with clinical assessments of rigidity prove inconsistent [5]. In addition, force measurements are highly sensitive to the location of the elbow joint within the actuator [4], and most devices are not usable with other joints, limiting the clinical use of these devices.In this study, we developed a multijoint rigidity-testing device to enable objective and quantitative measures of rigidity with millisecond resolution. The investigator passively manipulated the subject's joints while stabilizing the appendage distal to the joint with two opposing force transducers, providing a measurement of differential force during the movement. These forces were synchronized to the joint angle, measured by a motion capture camera system. Here, we show feasibility data for detecting changes in muscle rigidity in a parkinsonian nonhuman primate treated with Sinemet or subthalamic nucleus (STN) DBS.The device was composed of two FC 2231 load cells (Measurement Specialities, Hampton, VA) attached to the researcher's thumb and index/middle finger with Velcro straps (Velcro, Manchester, NH). Two metal stabilization plates were secured to opposite sides of the primate's limb with an adjustable Velcro strap. Indentations in the plates provided a stable point for pressure application of the load cells and minimized forces and torques in unwanted directions. The Velcro attachments allow the rigidity-testing device to fit any size limb in order to test a variety of joints. The data from the sensors were transmitted to an Arduino Uno microcontroller (Ivrea, Italy). Position data were collected using an infrared motion capture system (Vicon, Centennial, CO) as shown in Fig. 1(a). Reflective markers were placed at strategic locations on the body of the nonhuman primate as well as on the researcher's hand. An analog synchronization output from the Vicon system was sent to the Arduino microcontroller to coregister the force and position data offline (Fig. 1(c)). All analysis was performed offline in Matlab (v2012b, Natick MA). The differential force signal from the load cells and the position signals were low-pass filtered (fifth order butterworth, cutoff 12.5 Hz).Rigidity testing was performed on a single rhesus monkey (Macacca mulatta, 19 y.o., ♀) that was previously rendered parkinsonian with three daily injections of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP, Toronto Research Chemicals, Inc., Brisbane, ON, 0.4–0.6 mg/kg). To compare the effectiveness of therapies for Parkinsonism, rigidity was tested under three experimental conditions: (1) MPTP, (2) MPTP + DBS, and (3) MPTP + Sinemet. In condition 2, stimulation was turned on for at least 1 min before performing passive manipulations. After stimulation was terminated, Sinemet therapy was given through oral administration of 200 mg (150 mg levodopa, 50 mg carbidopa, Merck, Whitehouse Station, NJ). Passive manipulations were performed at least 60 min after drug administration to allow for drug absorption. Passive manipulations were conducted through the flexion and extension of several joints, including the wrist, elbow, shoulder, hip, knee, and ankle contralateral to the STN-DBS implant.The total force displacement, defined as the difference between the force measurements from the two load cells, was used as a surrogate measure of resistance to joint movement. An increased force was measured when more effort was necessary to move the limb, indicative of a more rigid state. The data were grouped into flexion and extension movement epochs, as shown in Fig. 1(e). The rigidity in the joint was characterized by the area between the flexion and extension curves, which has been shown to correlate with rigidity [4].In Fig. 2, the flexion and extension curves were averaged over ten joint articulations, and mean and standard error curves were plotted to describe the rigidity of the joint (Figs. 2(a)–2(g)). Consecutive manipulations with a consistent angle range were used to calculate flexion and extension for each joint-movement analysis for each experiment.Using the Wilcoxon rank-sum test adjusted for multiple comparisons, the device was able to capture changes in the rigidity between the three conditional states (rank-sum test, n = 10, p < 0.01). For the elbow, the smallest flexion-extension curve area was found in the MPTP + DBS trials, indicating that elbow rigidity was alleviated with STN-DBS more than with Sinemet (p = 0.0013). In contrast, knee rigidity was reduced with both STN-DBS and Sinemet, but there was no difference between the two therapies (p = 0.064). These results show the flexibility of the device for use on multiple joints.The objective of this research was to build an accurate, inexpensive, and easy-to-use rigidity measurement tool to quantifying Parkinsonian rigidity. The results indicate that the rigidity-testing device presented here is sensitive enough to measure changes in rigidity resulting from dopamine replacement and DBS therapies. Furthermore, the unique handheld differential load cell design allows for testing of multiple joints (wrist, elbow, shoulder, ankle, knee, hip) and has potential to be extended further.Currently, clinicians use UPDRS to measure rigidity in Parkinson's disease patients, but the scale has a small range and is subjective to interclinician variability. Future iterations of this rigidity-testing device could be used in the clinic to assist neurologists in titrating medication levels and DBS parameters. However, future iterations will also need to address several limitations of the current device, including the inability to quantify transverse torque at the point of contact of the sensor, confounding forces due to the subject actively resisting during manipulations, and possible variability in measurement due to inconsistent placement of the sensors on the subject's limbs between trials.This material is based upon work supported by NIH through an R44 NS060269, the NSF Graduate Research Fellowship under Grant No. 00006595, and by the NSF IGERT under DGE-1069104. We thank Filippo Agnesi, David Moreno, and Annalise Colton (University of Minnesota) for their technical assistance.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.345
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2014
Admission routes1
Has abstractyes

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